...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Communications in Numerical Methods in Engineering >On the use of machine learning techniques for the mechanical characterization of soft biological tissues
【24h】

On the use of machine learning techniques for the mechanical characterization of soft biological tissues

机译：关于使用机器学习技术对软生物组织进行机械表征

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees, and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden, and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the strain energy function and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted; thus, the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues.
机译：在寻找适合材料模型的新策略的推动下，本工作探索了一种新方法。提出了使用基于机器学习技术的数值和复杂算法（例如用于回归的支持向量机，袋装决策树和人工神经网络）来解决软生物组织本构定律的参数识别问题。首先，使用分析性单轴数据（周向和纵向）作为输入对数学工具进行训练，并将其对应的Gasser，Ogden和Holzapfel应变能函数的材料参数作为输出。在训练过程中，训练和测试错误在发现输入和输出之间的相关性方面显示出极大的效率；此外，相关系数非常接近1。其次，该工具通过对周向和纵向单轴分析数据的不可见观察进行了验证。结果表明，应变能函数的材料参数的预测与分析曲线之间有很好的一致性。最后，拟合了来自不同心血管组织的实际圆周和纵向单轴测试的数据。因此，可以预测这些组织的材料模型。我们发现该方法能够始终如一地识别模型参数，并且我们相信使用这些数字工具可以改善软生物组织的表征。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号